摘要
结合多传感器数据融合技术和矢量水听器的目标定向原理。提出了一种水下目标定向估计的改进算法。该算法仍然具有声压阵速联合信息处理所具有的抗各向同性非相干干扰的能力。针对传统平均声强器采用算术平均方法估计被测目标声强的缺点，充分利用被测样本数据，采用基于方差的加权优化数据融合方法估计声强。仿真实验说明。定向融合算法在目标方向0～360°范围和噪声0-15dB的情况下，比传统的平均声强器具有更高的定向精度．对水下目标检测工程有重要的应用意义．
An improved bearing estimation algorithm used in vector hydrophone is developed, combining the multi-sensor data fusion technology and the sensor bearing principle. The improved algorithm is still based on pressure and particle velocity combined processing, so can effectively suppress isotropie non-coherent interference. Against the acoustic disadvantage of arithmetic average, the fusion algorithm makes full use of the data samples and estimates the target acoustic intensity using the weighted variance optimization fusion technology. The simulation results showed that under the conditions of target direction from 0 to 360 degree and noise from 0 to 15dB, the fusion algorithm can make higher accurate bearing estimation than the traditional acoustic intensity averager, which is significant for underwater target detection project.
出处
《海洋技术》
北大核心
2009年第3期59-61,66,共4页
Ocean Technology
基金
国家高技术研究发展计划(863计划)资助项目(2006AA09Z144)
山东省科学院博士基金资助项目(200605)
关键词
矢量水听器
水下目标检测
定向
数据融合
vector hydrophone
underwater target detection
bearing
data fusion
